509 lines
20 KiB
C++
509 lines
20 KiB
C++
#include "TrainStepOneEngine.h"
|
||
#include <opencv2/opencv.hpp>
|
||
#include "myutils.h"
|
||
#include "myqueue.h"
|
||
|
||
using namespace ai_matrix;
|
||
|
||
TrainStepOneEngine::TrainStepOneEngine() {}
|
||
|
||
TrainStepOneEngine::~TrainStepOneEngine() {}
|
||
|
||
APP_ERROR TrainStepOneEngine::Init()
|
||
{
|
||
bUseEngine_ = MyUtils::getins()->ChkIsHaveTarget("NUM");
|
||
if (!bUseEngine_)
|
||
{
|
||
LogInfo << "engineId_:" << engineId_ << " not use engine";
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
strPort0_ = engineName_ + "_" + std::to_string(engineId_) + "_0";
|
||
modelConfig_ = MyYaml::GetIns()->GetModelConfig("TrainStepOneEngine");
|
||
iSpaceMinRBXPer_ = MyYaml::GetIns()->GetIntValue("gc_space_minrbx_imgpercent");
|
||
|
||
// 读取模型信息
|
||
APP_ERROR ret = ReadModelInfo();
|
||
if (ret != APP_ERR_OK)
|
||
{
|
||
LogError << "Failed to read model info, ret = " << ret;
|
||
return ret;
|
||
}
|
||
ret = InitModel();
|
||
if (ret != APP_ERR_OK)
|
||
{
|
||
LogError << "Failed to read model info, ret = " << ret;
|
||
return ret;
|
||
}
|
||
|
||
// 获取几个摄像头识别车号；是否有单独识别平车的摄像头(即定检期摄像头是否识别平车)
|
||
std::map<int, ai_matrix::DataSourceConfig> mapUseDataSouceCfg = MyYaml::GetIns()->GetUseDataSourceConfig();
|
||
for (auto iter = mapUseDataSouceCfg.begin(); iter != mapUseDataSouceCfg.end(); iter++)
|
||
{
|
||
if (iter->second.strTarget.find("NUM") != std::string::npos)
|
||
{
|
||
LogDebug << "DataSource:" << iter->first << " deal NUM";
|
||
mapDataSourceCfg_[iter->first] = iter->second;
|
||
|
||
if (iter->second.strTarget.find("CHKDATE") != std::string::npos ||
|
||
iter->second.strTarget.find("FLAT") != std::string::npos)
|
||
{
|
||
LogDebug << "sourceid:" << iter->first << " Target:" << iter->second.strTarget;
|
||
mapSameSideFlatcarSid_[iter->first] = iter->first;
|
||
}
|
||
}
|
||
}
|
||
|
||
// 设置同侧识别平车摄像头信息
|
||
for (auto iter = mapDataSourceCfg_.begin(); iter != mapDataSourceCfg_.end(); iter++)
|
||
{
|
||
if (mapSameSideFlatcarSid_.find(iter->first) != mapSameSideFlatcarSid_.end())
|
||
{
|
||
continue;
|
||
}
|
||
for (auto iterFlat = mapSameSideFlatcarSid_.begin(); iterFlat != mapSameSideFlatcarSid_.end(); iterFlat++)
|
||
{
|
||
ai_matrix::DataSourceConfig dataSourceCfg = MyYaml::GetIns()->GetDataSourceConfigById(iterFlat->first);
|
||
if (dataSourceCfg.iLeftFirst == iter->second.iLeftFirst)
|
||
{
|
||
mapSameSideFlatcarSid_[iter->first] = iterFlat->first;
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
LogInfo << "TrainStepOneEngine Init ok";
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
APP_ERROR TrainStepOneEngine::InitModel()
|
||
{
|
||
modelinfo.yolov5ClearityModelParam.uiClassNum = class_num;
|
||
modelinfo.yolov5ClearityModelParam.uiClearNum = clear_num;
|
||
modelinfo.yolov5ClearityModelParam.uiDetSize = det_size;
|
||
modelinfo.yolov5ClearityModelParam.fScoreThreshold = score_threshold;
|
||
modelinfo.yolov5ClearityModelParam.fNmsThreshold = nms_threshold;
|
||
|
||
modelinfo.modelCommonInfo.uiModelWidth = model_width;
|
||
modelinfo.modelCommonInfo.uiModelHeight = model_height;
|
||
modelinfo.modelCommonInfo.uiInputSize = input_size;
|
||
modelinfo.modelCommonInfo.uiOutputSize = output_size;
|
||
modelinfo.modelCommonInfo.uiChannel = INPUT_CHANNEL;
|
||
modelinfo.modelCommonInfo.uiBatchSize = batch_size;
|
||
modelinfo.modelCommonInfo.strInputBlobName = INPUT_BLOB_NAME;
|
||
modelinfo.modelCommonInfo.strOutputBlobName = OUTPUT_BLOB_NAME;
|
||
|
||
string strModelName = "";
|
||
|
||
int nRet = yolov5model.YoloV5ClearityInferenceInit(&modelinfo, strModelName, modelConfig_.strOmPath);
|
||
if (nRet != 0)
|
||
{
|
||
LogError << "YoloV5ClassifyInferenceInit nRet:" << nRet;
|
||
return APP_ERR_COMM_READ_FAIL;
|
||
}
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
APP_ERROR TrainStepOneEngine::ReadModelInfo()
|
||
{
|
||
char szAbsPath[PATH_MAX];
|
||
// Get the absolute path of model file
|
||
if (realpath(modelConfig_.strOmPath.c_str(), szAbsPath) == nullptr)
|
||
{
|
||
LogError << "Failed to get the real path of " << modelConfig_.strOmPath.c_str();
|
||
return APP_ERR_COMM_NO_EXIST;
|
||
}
|
||
|
||
// Check the validity of model path
|
||
int iFolderExist = access(szAbsPath, R_OK);
|
||
if (iFolderExist == -1)
|
||
{
|
||
LogError << "ModelPath " << szAbsPath << " doesn't exist or read failed!";
|
||
return APP_ERR_COMM_NO_EXIST;
|
||
}
|
||
|
||
//读取模型参数信息文件
|
||
Json::Value jvModelInfo;
|
||
if (!MyUtils::getins()->ReadJsonInfo(jvModelInfo, modelConfig_.strModelInfoPath))
|
||
{
|
||
LogError << "ModelInfoPath:" << modelConfig_.strModelInfoPath << " doesn't exist or read failed!";
|
||
return APP_ERR_COMM_NO_EXIST;
|
||
}
|
||
|
||
model_width = jvModelInfo["model_width"].asInt();
|
||
model_height = jvModelInfo["model_height"].asInt();
|
||
clear_num = jvModelInfo["clear"].isArray() ? jvModelInfo["clear"].size() : 0;
|
||
class_num = jvModelInfo["class"].isArray() ? jvModelInfo["class"].size() : 0;
|
||
input_size = GET_INPUT_SIZE(model_width, model_height);
|
||
output_size = GET_OUTPUT_SIZE(model_width, model_height, clear_num, class_num);
|
||
det_size = clear_num + class_num + 5;
|
||
score_threshold = modelConfig_.fScoreThreshold;
|
||
nms_threshold = modelConfig_.fNMSTreshold;
|
||
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
APP_ERROR TrainStepOneEngine::DeInit()
|
||
{
|
||
if (!bUseEngine_)
|
||
{
|
||
LogInfo << "engineId_:" << engineId_ << " not use engine";
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
yolov5model.YoloV5ClearityInferenceDeinit();
|
||
LogInfo << "TrainStepOneEngine DeInit ok";
|
||
return APP_ERR_OK;
|
||
}
|
||
|
||
/**
|
||
* push数据到队列，队列满时则休眠一段时间再push
|
||
* inParam : const std::string strPort push的端口
|
||
: const std::shared_ptr<ProcessData> &pProcessData push的数据
|
||
* outParam: N/A
|
||
* return : N/A
|
||
*/
|
||
void TrainStepOneEngine::PushData(const std::string &strPort, const std::shared_ptr<ProcessData> &pProcessData)
|
||
{
|
||
while (true)
|
||
{
|
||
int iRet = outputQueMap_[strPort]->push(std::static_pointer_cast<void>(pProcessData));
|
||
if (iRet != 0)
|
||
{
|
||
LogError << " frameid:" << pProcessData->iFrameId << " push fail iRet:" << iRet;
|
||
if (iRet == 2)
|
||
{
|
||
usleep(10000); // 10ms
|
||
continue;
|
||
}
|
||
}
|
||
break;
|
||
}
|
||
}
|
||
|
||
/**
|
||
* 过滤无效信息
|
||
* inParam : std::vector<stDetection> &vecRet :识别结果数据
|
||
: std::shared_ptr<ProcessData> pProcessData :帧信息数据
|
||
* outParam: N/A
|
||
* return : N/A
|
||
*/
|
||
void TrainStepOneEngine::FilterInvalidInfo(std::vector<stDetection> &vecRet, std::shared_ptr<ProcessData> &pProcessData)
|
||
{
|
||
ai_matrix::DataSourceConfig dataSourceCfg = mapDataSourceCfg_[pProcessData->iDataSource];
|
||
std::vector<stDetection> vecSpaceInfo;
|
||
for (auto it = vecRet.begin(); it != vecRet.end();)
|
||
{
|
||
LogDebug << "frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id << " ltx:" << it->bbox[0] << " lty:" << it->bbox[1]
|
||
<< " rbx:" << it->bbox[2] << " rby:" << it->bbox[3];
|
||
// 根据配置文件中 设置的识别范围，过滤掉无效数据
|
||
if (!(it->bbox[0] >= dataSourceCfg.fIdentifyAreasLTX &&
|
||
it->bbox[1] >= dataSourceCfg.fIdentifyAreasLTY &&
|
||
it->bbox[2] <= dataSourceCfg.fIdentifyAreasRBX &&
|
||
it->bbox[3] <= dataSourceCfg.fIdentifyAreasRBY))
|
||
{
|
||
LogDebug << "frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id << " 超出识别区域-识别区域：("
|
||
<< dataSourceCfg.fIdentifyAreasLTX << "," << dataSourceCfg.fIdentifyAreasLTY << "),("
|
||
<< dataSourceCfg.fIdentifyAreasRBX << "," << dataSourceCfg.fIdentifyAreasRBY << ")";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
// 如果设置了不识别车头，则去掉车头标记的大框
|
||
if (!MyYaml::GetIns()->GetBoolValue("gc_train_heard_detect") && it->class_id == TRAIN_HEAD)
|
||
{
|
||
LogDebug << "frameId:" << pProcessData->iFrameId << " 过滤掉车头编号";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
// 去除车头时的非车头编号信息
|
||
if(pProcessData->nMonitorState == MONITOR_MODEL_TRAIN_HEAD )
|
||
{
|
||
if(it->class_id != TRAIN_HEAD)
|
||
{
|
||
LogDebug << " 帧号:" << pProcessData->iFrameId
|
||
<< " 大类:" << it->class_id << " 识别于车头位置，无效！";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
}
|
||
|
||
// 去除车尾的车头编号信息
|
||
if (pProcessData->nMonitorState != MONITOR_MODEL_TRAIN_HEAD)
|
||
{
|
||
if (it->class_id == TRAIN_HEAD)
|
||
{
|
||
LogDebug << " 帧号:" << pProcessData->iFrameId
|
||
<< " 大类:" << it->class_id << " 识别于非车头位置，无效！";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
}
|
||
|
||
// 去除车尾的间隔信息
|
||
if (pProcessData->nMonitorState == MONITOR_MODEL_TRAIN_TAIL
|
||
&& ((it->class_id >= 9 && it->class_id <= 17 && it->class_id != 15) || it->class_id == 18))
|
||
{
|
||
LogDebug << " frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id
|
||
<<" 识别于车尾部分,无效！";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
// 过滤掉识别于模型反馈无车状态下的所有大框信息
|
||
if (pProcessData->nMonitorState == MONITOR_MODEL_NO_TRAIN)
|
||
{
|
||
LogDebug << " frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id
|
||
<<" 识别于模型反馈的无车状态下,无效！";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
// 按大框高度剔除远股道识别的信息
|
||
int iClassHeight = it->bbox[3] - it->bbox[1];
|
||
if (dataSourceCfg.mapClassMinH.find(it->class_id) != dataSourceCfg.mapClassMinH.end() &&
|
||
iClassHeight < dataSourceCfg.mapClassMinH[it->class_id])
|
||
{
|
||
LogDebug << " frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id << " iClassHeight:" << iClassHeight
|
||
<< " minH:" << dataSourceCfg.mapClassMinH[it->class_id] << " 过滤疑似远股道识别";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
//如果有平车摄像头，则平车摄像头只识别大框类别1，6。
|
||
auto iterFlat = mapSameSideFlatcarSid_.find(pProcessData->iDataSource);
|
||
if (iterFlat != mapSameSideFlatcarSid_.end() && pProcessData->iDataSource == iterFlat->second)
|
||
{
|
||
if (it->class_id != 1 && it->class_id != 6)
|
||
{
|
||
LogDebug << " frameId:" << pProcessData->iFrameId << " flat camera only deal 1 or 6";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
}
|
||
|
||
//剔除高度大于宽的车号大框
|
||
if (((it->class_id >= 2 && it->class_id <= 6) || it->class_id == J_TRAIN_NUM || it->class_id == W_TRAIN_NUM) &&
|
||
(it->bbox[3] - it->bbox[1]) > (it->bbox[2] - it->bbox[0]))
|
||
{
|
||
LogWarn << " frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id << " 过滤 高度大于宽度的车号";
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
if (((it->class_id >= 2 && it->class_id <= 6) || it->class_id == J_TRAIN_NUM || it->class_id == W_TRAIN_NUM) &&
|
||
(it->bbox[3] - it->bbox[1]) < MyYaml::GetIns()->GetIntValue("gc_num_frame_height"))
|
||
{
|
||
LogWarn << "疑似误识别到远股道车号，帧号:" << pProcessData->iFrameId
|
||
<< "大框高度:" << (it->bbox[3] - it->bbox[1]);
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
if ((it->class_id == 1 || it->class_id == TRAIN_PRO)
|
||
&& (it->bbox[3] - it->bbox[1]) < MyYaml::GetIns()->GetIntValue("gc_pro_frame_height")) {
|
||
LogWarn << "疑似误识别到远股道属性，帧号:" << pProcessData->iFrameId
|
||
<< "大框高度:" << (it->bbox[3] - it->bbox[1]);
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
if ((it->class_id == 9 || it->class_id == C_TRAIN_SPACE)
|
||
&& (it->bbox[2] - it->bbox[0]) > MyYaml::GetIns()->GetIntValue("gc_c_space_frame_width")) {
|
||
LogWarn << "疑似误识别到敞车间隔，帧号:" << pProcessData->iFrameId
|
||
<< "大框宽度:" << (it->bbox[2] - it->bbox[0]);
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
|
||
//补连塔的相机比较近，间隔基本在画面底部，因此当间隔比较靠画面上时过滤掉。
|
||
if ((it->class_id >= 9 && it->class_id <= 17 && it->class_id != 15) || it->class_id == U_TRAIN_SPACE)
|
||
{
|
||
if (it->bbox[3] < (pProcessData->iHeight * iSpaceMinRBXPer_ / 100))
|
||
{
|
||
LogWarn << " frameId:" << pProcessData->iFrameId
|
||
<< " bigclassid:" << it->class_id << " 过滤间隔过于靠下的间隔信息 fRBY:" << it->bbox[3];
|
||
it = vecRet.erase(it);
|
||
continue;
|
||
}
|
||
vecSpaceInfo.emplace_back(*it);
|
||
}
|
||
|
||
++it;
|
||
}
|
||
|
||
//主摄像头1帧如果只识别2个大框，如果非平车的车号和属性场景，则必有间隔框
|
||
if (pProcessData->iDataSource == 0 && vecRet.size() == 2)
|
||
{
|
||
int iHeight0 = vecRet[0].bbox[1] / 2 + vecRet[0].bbox[3] / 2;
|
||
int iHeight1 = vecRet[1].bbox[1] / 2 + vecRet[1].bbox[3] / 2;
|
||
int iCenterY = pProcessData->iHeight / 2;
|
||
if (iHeight0 < iCenterY && iHeight1 < iCenterY) //非平车
|
||
{
|
||
if (!((vecRet[0].class_id >= 9 && vecRet[0].class_id <= 17 && vecRet[0].class_id != 15) || vecRet[0].class_id == U_TRAIN_SPACE) &&
|
||
!((vecRet[1].class_id >= 9 && vecRet[1].class_id <= 17 && vecRet[1].class_id != 15) || vecRet[1].class_id == U_TRAIN_SPACE))
|
||
{
|
||
LogDebug << " frameId:" << pProcessData->iFrameId << " no space";
|
||
vecRet.clear();
|
||
}
|
||
}
|
||
}
|
||
|
||
// //K车间隙大，导致识别对面股道信息的过滤。 获取间隔最宽的大框，跟该大框X轴有重合的过滤掉。
|
||
// if (vecSpaceInfo.size() > 0 && vecRet.size() > 1)
|
||
// {
|
||
// std::sort(vecRet.begin(), vecRet.end(), [](stDetection &a, stDetection &b){return (a.bbox[2]-a.bbox[0]) > (b.bbox[2]-b.bbox[0]);});
|
||
// auto iterMaxWSpace = vecSpaceInfo.begin();
|
||
// if (iterMaxWSpace->class_id == K_TRAIN_SPACE)
|
||
// {
|
||
// for (auto it = vecRet.begin(); it != vecRet.end();)
|
||
// {
|
||
// if ((it->class_id <= 6 || it->class_id == W_TRAIN_NUM) && !(it->bbox[2] <= iterMaxWSpace->bbox[0] || it->bbox[0] >= iterMaxWSpace->bbox[2]))
|
||
// {
|
||
// LogError << "sourceid:" << pProcessData->iDataSource << " frameId:" << pProcessData->iFrameId
|
||
// << " classid:" << it->class_id << " overlap x";
|
||
// it = vecRet.erase(it);
|
||
// continue;
|
||
// }
|
||
// ++it;
|
||
// }
|
||
// }
|
||
// }
|
||
}
|
||
|
||
/**
|
||
* 设置大框类型
|
||
* inParam : PostSubData &postSubData :推理结果
|
||
* outParam: PostSubData &postSubData :推理结果
|
||
* return : N/A
|
||
*/
|
||
void TrainStepOneEngine::SetTargetType(PostSubData &postSubData)
|
||
{
|
||
if (postSubData.iBigClassId == TRAIN_HEAD)
|
||
{
|
||
postSubData.iTargetType = HEAD;
|
||
}
|
||
else if (postSubData.iBigClassId == TRAIN_PRO)
|
||
{
|
||
postSubData.iTargetType = PRO;
|
||
}
|
||
else if ((postSubData.iBigClassId >= 2 && postSubData.iBigClassId <= 6) ||
|
||
postSubData.iBigClassId == J_TRAIN_NUM ||
|
||
postSubData.iBigClassId == W_TRAIN_NUM)
|
||
{
|
||
postSubData.iTargetType = NUM;
|
||
}
|
||
else if (postSubData.iBigClassId >= 9 && postSubData.iBigClassId <= 17 && postSubData.iBigClassId != 15)
|
||
{
|
||
postSubData.iTargetType = TRAINSPACE;
|
||
}
|
||
else if (postSubData.iBigClassId == U_TRAIN_SPACE)
|
||
{
|
||
postSubData.iTargetType = SPACE;
|
||
}
|
||
}
|
||
|
||
APP_ERROR TrainStepOneEngine::Process()
|
||
{
|
||
if (!bUseEngine_)
|
||
{
|
||
LogInfo << "engineId_:" << engineId_ << " not use engine";
|
||
return APP_ERR_OK;
|
||
}
|
||
int iRet = APP_ERR_OK;
|
||
|
||
while (!isStop_)
|
||
{
|
||
std::shared_ptr<void> pVoidData0 = nullptr;
|
||
inputQueMap_[strPort0_]->pop(pVoidData0);
|
||
if (nullptr == pVoidData0)
|
||
{
|
||
usleep(1000); //1ms
|
||
continue;
|
||
}
|
||
|
||
std::shared_ptr<ProcessData> pProcessData = std::static_pointer_cast<ProcessData>(pVoidData0);
|
||
//组织输出数据
|
||
std::shared_ptr<PostData> pPostData = std::make_shared<PostData>();
|
||
pPostData->iModelType = MODELTYPE_NUM;
|
||
pPostData->nMonitorState = pProcessData->nMonitorState; //来车检测的四个分类
|
||
|
||
//获取图片
|
||
if (pProcessData->iStatus == TRAINSTATUS_RUN || pProcessData->bIsEnd)
|
||
{
|
||
if (pProcessData->pData != nullptr && pProcessData->iSize != 0)
|
||
{
|
||
cv::Mat img(pProcessData->iHeight, pProcessData->iWidth, CV_8UC3, static_cast<uint8_t *>(pProcessData->pData.get())); //BGR
|
||
|
||
//进行推理
|
||
std::vector<stDetection> res;
|
||
yolov5model.YoloV5ClearityInferenceModel(img, res);
|
||
|
||
//过滤无效信息
|
||
FilterInvalidInfo(res, pProcessData);
|
||
|
||
MyUtils::getins()->GetMaxScoreResult(res);
|
||
//整理推理结果
|
||
//根据非极大值抑制的结果标注相关信息(画框,文字信息等)
|
||
//res.size()为每张图片上的识别到的对象数目
|
||
for (size_t j = 0; j < res.size(); j++)
|
||
{
|
||
/*
|
||
[0:车头; 1:属性; 2:煤炭漏斗车(兖矿自备,枣矿自备); 3:敞车; 4:棚车; 5:罐车; 6:平车
|
||
7:定检期; 8:牲畜车; 9:敞车间隔; 10:自备车间隔; 11:平车间隔; 12:罐车间隔; 13:棚车车间隔;
|
||
14:牲畜车间隔; 15:毒品车; 16: 毒品车间隔; 17:混合车厢间隔; 18:连接轴通用间隔]
|
||
*/
|
||
if (res[j].class_id < 0 || res[j].class_id > 18)
|
||
{
|
||
continue;
|
||
}
|
||
SingleData singledata;
|
||
// singledata.iLine = -1;
|
||
singledata.iClassId = res[j].class_id;
|
||
singledata.fScore = res[j].class_conf;
|
||
// singledata.iAnchorId = -1;
|
||
singledata.fLTX = res[j].bbox[0];
|
||
singledata.fLTY = res[j].bbox[1];
|
||
singledata.fRBX = res[j].bbox[2];
|
||
singledata.fRBY = res[j].bbox[3];
|
||
singledata.fClear = res[j].clear_id;
|
||
|
||
PostSubData postSubData;
|
||
postSubData.iBigClassId = res[j].class_id;
|
||
postSubData.vecSingleData.emplace_back(singledata);
|
||
postSubData.step1Location.fLTX = res[j].bbox[0];
|
||
postSubData.step1Location.fLTY = res[j].bbox[1];
|
||
postSubData.step1Location.fRBX = res[j].bbox[2];
|
||
postSubData.step1Location.fRBY = res[j].bbox[3];
|
||
SetTargetType(postSubData);
|
||
pPostData->vecPostSubData.emplace_back(postSubData);
|
||
|
||
// LogDebug << "数据源:" << pProcessData->iDataSource << " 帧:" << pProcessData->iFrameId
|
||
// << " --iClassId:" << singledata.iClassId << " iLine:" << singledata.iLine << " confidence=" << singledata.fScore
|
||
// << " lx=" << singledata.fLTX << " ly=" << singledata.fLTY << " rx=" << singledata.fRBX << " ry=" << singledata.fRBY
|
||
// << " clear:" << singledata.fClear;
|
||
}
|
||
}
|
||
}
|
||
|
||
//及时释放内存
|
||
if (pProcessData->pData != nullptr)
|
||
{
|
||
pProcessData->pData = nullptr;
|
||
pProcessData->iSize = 0;
|
||
}
|
||
|
||
//push端口0，第1步推理
|
||
pProcessData->pVoidData = std::static_pointer_cast<void>(pPostData);
|
||
PushData(strPort0_, pProcessData);
|
||
}
|
||
return APP_ERR_OK;
|
||
}
|
||
|